Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures

TL;DR

This paper introduces a new gradient-based measure for event detection in HMMs that is accurate, robust, and versatile, improving performance in identifying skills and anomalies in autonomous systems.

Contribution

It provides a theoretical link between HMM belief state derivatives and emission probabilities, enabling improved event detection methods.

Findings

Outperforms state-of-the-art methods across all metrics

Proven theoretical relationship between belief state derivatives and emissions

Applicable to various domains using HMMs for event detection

Abstract

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.